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Attendance monitoring is an essential task for any organization and traditional methods such as pen and paper or electronic card systems have their limitations with advancements. The proposed model is a real time face presence monitoring system using Raspberry Pi. The system captures image of individuals using camera and processes the images using deep learning algorithms to detect faces. These faces are then matched against a pre-existing database of known individuals. Door interlock controls add an extra layer of security, ensuring that only authorized person have access to restricted areas. The system provides a user-friendly interface to view everyone's attendance records and generate attendance management reports. The proposed system provides a reliable and accurate solutions for attendance tracking and can be used in various organizations. [ABSTRACT FROM AUTHOR]

A growing field in India that can assist farmers in many ways is the identification of plant diseases. Plant disease reduces the mass production in agriculture. Every time due to heavy loss in agriculture, farmers face many challenges to raise up their needs. This study analyzes various diseases that affect various plants due to the following criteria like environmental condition, types of plant formed from Monocot and Dicot family, crop rotation and Ph value of the soil. The existing system analyzed the diseases based on one of the criteria. Although the strategy is to be followed to identify plant diseases at the early stage with various aspects so that the production in agriculture could be increased. The majority of machine learning algorithms already exist and may be improved upon to yield more accuracy. Xanthomonas wilt, Black and Yellow Sigatoka, Bunchy Top Virus, Black Sigatoka, Fusarium wilt, Cassava Bacterial Blight (CBB), Cassava Brown Steak Disease (CBSD), Cassava Green Mite (CGM), and Cassava Mosaic Disease (CMD), Yellow vein mosaic virus, and Powdery Mildew are a few of the diseases of the plants grown on crop rotation which are the subject of study of this paper. Plant illnesses are classified according to disease in the many open-source datasets that are accessible. For deeper understanding, we have provided a list of references. Here, we've examined the approaches that are now in use for automatically detecting various diseases of plant leaves. We present an approach for accurately identifying early signs of plant diseases from a variety of perspectives. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) and Machine Learning (ML) transforming the Insurance industry by improving efficiency, reducing costs, and providing a better customer experience. As these technologies continue to evolve, more innovation can be expected in the Insurance industry. A Bibliographic analysis is conducted for scientific mapping based on 1,084 SCOPUS-indexed publications between the year 2000-2022 using VOSviewer Application. The Analysis was conducted based on Publications by year, Source, Author, Affiliation, Country, Type, Subject Area and Funding Sponsors. The research found the result, the year 2022 had the highest publication of 203, through documents per year by the source of Lecture Notes In Computer Science Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics at 43 publications, Bauder, R.A. author is the maximum contributor, Harvard Medical School has been the major affiliate, United States of America, has the maximum 341 publications, Conference paper has the majority participation at 412 documents at 38 percent and Keywords "Artificial intelligence", Machine Learning" and "Insurance" has the highest occurrence. The maximum number of publications inthe field of computer science at 28 percent. Overall, this bibliographic analysis provides a comprehensive overview of the current state of research in AI and Machine Learning in the Insurance industry and highlights the potential for further innovation and development in this field. [ABSTRACT FROM AUTHOR]

Floods in Malaysia are described as flash floods and monsoon floods by the Department of Irrigation and Drainage (DID). Forecasting is a specialized form of predictive analysis used to predict future trends or behavior from existing data. Weather forecasting predicts the weather in the future from current data circumstances. As flood forecasting models become more accurate, however, their capacity to accurately predict flooding decreased as the forecast continues. Therefore, to understand the different techniques used to forecast flood levels, a systematic review of the literature was conducted. This paper's main objective is to examine the most common variable used to forecast floods, utilizing the systematic review technique. From the main focus, we can identify the research questions, such as the most commonly used prediction method and its accuracy. In the end, two of the most common variables used for flood forecasting are rainfall and water level. This study's data can help others forecast floods using standard variables that yield the best accuracy possible. [ABSTRACT FROM AUTHOR]

In most nations, lung cancer (LC) is the main cause to mortality cases occurring due to tumors to males and females. Lung nodules (masses) are recognized through digital technologies support precise diagnosis using medical imaging techniques such as CT-scan, X-rays and MRI. As the nodules are normally connected to the blood vessels, identification of lung nodules is a difficult task. Several studies illustrate that diagnosing or detecting such cases earlier is regarded the best successful method to counter this disease. Many systems have been developed that automatically identify lung tumors by using magnetic resonance spectroscopy and pattern recognition methods. Despite the encouraging consistency of the classification, none of these schemes have set their course in clinical practice, which also concerns the lack of clarity concerning the process of taking decisions. In this paper, we present a systematic mapping study on lung tumors recognition. After four sorting methods, we obtained 375 relevant studies in total. After applying four filters manually, 48 papers as primary studies related to the main topic are selected as listed in Appendix (A). The selected papers classified them with respect to several facets. The results provide an overview of the existing relevant studies in reported in the literature, highlight focus areas and research gaps. [ABSTRACT FROM AUTHOR]

There has been a substantial growth of technology and it has evolved a lot, and with the evolving technology credit cards seem to be replacing cash payments. There is no second thought about credit cards getting used on a large scale but this extensive use brings a huge number of fraudulent uses of credit cards. In this paper, discussions on different technologies and methods that are used to detect and prevent credit card fraud have been presented. In the paper discussion on the statistics of the credit card frauds detected using machine learning and deep learning has been presented. In this paper, we have presented the survey on Machine learning techniques and analyzed their accuracy and speed, also we have discussed the methods that are used in detecting credit card fraud using deep learning and artificial intelligence. Furthermore, in this paper, various types of datasets are explained in detail. Also, the paper highlights the challenges and possible solutions to detect fraud. [ABSTRACT FROM AUTHOR]

In this world of ever-increasing texts, chats and literature users nowadays are posting countless comments on various social media platforms, news websites, and discussion forums. While some comments bring the positive side of the matter, whileother remarks are detrimental or combative in nature. Manually monitoring of such a large volume of comments and chats is a tedious and a time taking task. Therefore, various machine learning based techniques are employed to cater the task of detecting the levels of toxicity in the comments. We gathered information from 20 primary research papers that were found to be relevant in the field of toxic comment classification. We explored all the papers thoroughly taking into consideration the publishing date andconferences in which they were published. We noted the data set used, performance criteria, machine learning techniques employed and other important features such as diversity of toxicity classes and comparative report stated in them. We concluded our work with a detailed overview of the existing issues and a suggestion list of future topics that could be explored and implemented to make the system more accurate in classifying the toxic comments. [ABSTRACT FROM AUTHOR]

The easy accessibility of social media to everyone generates serious problems. Misleading news affect the mental health of peoples. False news can easily be created and propagated using online platform by using an Un-anonymous account. it is required to control the spread of fake news on social media. In this paper we used several linguistic features for fake news classification along with machine learning model. The linguistic features used are number of characters, number of words, noun-count, and number of articles. This study provides the heuristic solution by using both the news text and Linguistic features of text for better news classification. LIAR dataset is used for experiments. Our method outperforms the existing method. [ABSTRACT FROM AUTHOR]

Diabetes is one of the leading global health emergencies of the 21st century. The threatening side effects proves it to be a dangerous epidemic. This paper proposes a model to solve the problem in the existing system by comparing various data mining techniques in order to cluster and classify them. The paper studies a huge sample of diabetic patients common to diabetes and cardiovascular disorders. The method adopted is to cluster the data into three prior categories of high, medium and low clusters. Once clustered, optimum algorithmic technique is implemented to classify the data into mild and severe fragments in order to determine the chances of occurrence of a heart disorder due to the impact of diabetes. The aim is to implement an algorithm that can classify the data collected in order to infer whether or not a diabetic patient is prone to a heart disorder or not. [ABSTRACT FROM AUTHOR]

"Online learning is not the next big thing; it is the new big thing." "eLearning is changing. And we will see new models, new technologies, and designs emerge. So, let us drop the "e" – or at least give it a new and wider definition." These quotes influenced us to build this paper. During this unprecedented time, we all are using online resources. Our paper will enhance the teaching experience of the students. Especially school-going kids, it is the duty of teacher, influencer, and faculty to enhance the teaching experience so that, one can learn at their ease without any hustle. Our paper aims at building a website and android application (in the coming days) which will identify the individual words of any sentences entered. It will parse the string entered, remove the unwanted characters, and pass the remaining string to our AI model. As a result, the AI model will return the parts of speech the individual word belongs to. We have always seen students struggling to find that a given word is either a noun, or an adverb, or a preposition, and so on. We have searched the web but could not find any relevant websites doing the same task. There is a gigantic search engine on which we can search individual words but again, for sentences, people struggle. This may sound very simple for AI/ML enthusiasts but building a model and deploying that on a website and android application is not a cup of tea. Tech giants like Google, Facebook, and Microsoft are building different AI models and deploying them successfully on various platforms like Website, Android Application, iOS. However, we have done it successfully. [ABSTRACT FROM AUTHOR]

Recommendation systems (RS) suggest the right item to the right user. It predicts the user's rating to an item and based on this rating RS provides the suggestion to users. In today's world many online applications are already using the Recommendation system that provides a recommendation for a particular item like books, movies, music etc. in an automated fashion. This paper proposed a system that helps to find the best suitable Medicineaccording to diseasesby using library "recommenderlab" in R. This study proposed a system that gives the best medicine available according to the user rating available in database. User makes their decision according to their recommendation provides by the proposed system for finding best suitable medicine from available database [ABSTRACT FROM AUTHOR]

Many types of machines widely used in Industrial application process. The main objective is to recognize faults failure based on motor speed and vibration signal. To find fault machine with the use of vibration analysis in turning equipment and induction motor. Every defect can associated with cost and performance of the machine. Due to these associated risk such as cost, time, efficiency early detection, isolation and failure prediction is important. Using machine learning algorithm to predict the fault failure rate of machine easily. Signal level are monitored with vibration analysis. Depends upon the vibration signal faults are identified and classified. In this paper we discuss to classify the bearing the fault using different data mining techniques. To predict the imbalance condition, fault in bearing, and broken rotor bars faults are predicted using machine learning algorithm. [ABSTRACT FROM AUTHOR]

Most real-world classifications problem provides an uneven, skewed dataset. Imbalanced datasets are a key issue in machine learning. The challenge for any machine learning algorithm to learn from imbalanced dataset is correctly classifying cases from the minority class. Imbalanced dataset leads machine learning algorithms to misclassify cases from the minority class which is undesirable for real world classification applications and also degrades the performance of algorithm. Glass identification dataset is one of the datasets that serve as a example of skewed imbalance real world dataset. The majority of research on glass identificationtasks ignores the data outlier and class imbalance found in this dataset. Class imbalance, data outlier, and insignificant featuresare only a few of the factors that contribute to the poor performance of distance-based machine learning methods such as kNN. Thus, this paper explores the performance of one of the most simple and efficient machine learning classifier kNN. The authors performed interquartile range (IQR) thresholding to eliminate data outliers, ReliefF ranking to find significant features and SMOTE to cope with the class imbalance occurring in the dataset. The Inverse distance weighted kNN is utilized for classification in the improved dataset. The results revealed that using the strategies mentioned improves the performance of the kNN algorithm while also providing accuracy that is better and more dependable than much of the research done in this dataset. In this paper the authorsfound the highest accuracy of glass identification task is 78.923% with F-measure 0.791. [ABSTRACT FROM AUTHOR]

From the past few years spreading of the pests and diseases in plants have been increasing significantly. Paddy plants are the most important crop used in our country for the food, so it is most necessary to detect any disease of them within a short period of time for ensuring a proper and healthy growth of paddy plants. The process of manual disease detection requires labor and a large amount of time. Then utilizing the leaf images of plants for recognizing and classifying of diseases is the more focused research topic in the agriculture field. A survey on recognition and classification of paddy leaf diseases using image processing and machine learning techniques is presented in this paper based on the disease infected leaf images of paddy plants. Firstly the concept of various plant diseases and the standard process of plant disease detection are discussed in this paper. A study and survey on the totally 5 papers of work is carried out in detail by covering a work on leaf diseases of the paddy plants based on the certain criteria. Such criteria are different preprocessing techniques, various diseases/ classes, different segmentation methods, various classifiers and accuracy of employed techniques. The experimental results of these various techniques are compared and evaluated to design a best on detecting and classifying of paddy plant leaf diseases. [ABSTRACT FROM AUTHOR]

Modern Operating Systems have evolved with many enhanced features and optimizations. Although updates and features are constantly added to the systems to make our lives easier, the file management system remains behind the times. This project aims to make a File Management system to address modern-day user needs and make traditional file managers more adaptable to present times. We aim to develop this tool to incorporate contemporary requirements and perform functions unseen in conventional file managers. The project focuses on three main attributes: file and folder management, storage use reduction, and better accessibility for a user-friendly experience. In file management, some mundane tasks or processes require occasional attention and manual maintenance by the user. The idea behind this Intelligent File Manager isto reduce manual effort and make file management quick and simple with just a click. In terms of reduction in storage usage, insufficient storage issues arise over time. This forces users to browse through hundreds or thousands of files, folders, or images, only to realize that most of them are the same or way too similar, then delete them manually. Our Intelligent File System removes all these manual tasks and achieves storage optimization through automated detection and deletion of duplicate files. We strive to make the existing file system more simple, intuitive, and intelligent with better enhancements and features than its predecessors. We also intend to provide the user with a better experience that involves minimal to almost no manual work when working with file systems, with the help of Machine Learning and Automation Script [ABSTRACT FROM AUTHOR]

The Optimization of cost in any product is plays the important role. The Gradient descent method is used to solve the problem of local minimization. This is an Optimizing algorithm that also used for minimize the cost function in various machine learning. Basically we discussed the types of Gradient descent algorithm like Batch gradient descant, Stochastic gradient descant and Mini batch descant. This paper will be discussed on the different parts of Gradient descant and stochastic gradient descant algorithm. This paper is the comparative study of both algorithms. [ABSTRACT FROM AUTHOR]

Researchers use many classification algorithms with image filers to work on image classification. The goal of this study is to see how well machine learning classifiers perform with different WEKA image filters on image dataset. In this paper we present the comparison of different ML classifiers with image filters using Waikato Environment for Knowledge Analysis (WEKA). WEKA is open source data mining software that comprises of a collection of machine learning algorithms. This paper, we introduces four important data mining techniques K-NN,J48, Naive Bayes and Random forest classifiers for image classification with image filters on the birds image dataset using WEKA tool. In this paper, we compare these classifiers with image filers over different parameters and selected optimal classification algorithm. As per our results Random forest classifier working well on our small image dataset and we got 90% accuracy with image filters. To improve the performance of the classifiers we used ensemble technique. Then after we got 96% accuracy with ensemble image filters on this resultant image dataset. In this paper we concluded that ensemble technique will improve the performance of the classifiers with image filters. [ABSTRACT FROM AUTHOR]

Heart disease is a major killer in the world. Nature-inspired algorithms have been used as optimization techniques to predict the disease. The prediction of the disease can be further enhanced by using classification models of machine learning algorithms. The accuracy and other aspects of prediction varies with classification models. This paper presents the research conducted by combining nature-inspired techniques with machine learning algorithms to make intelligent decisions for prediction of heart diseases. As part of the research conducted, the prediction of the heart disease has been assessed by the combination of nature-inspired techniques and machine learning algorithms. This paper presents the outcome of the research conducted with four nature-inspired algorithms, namely Ant, Bat, Bee and Genetic algorithms and two machine learning classification models, Random Forest and SVM to predict the heart disease. Feature variables are extracted from heart data obtained from the UCI Machine Learning Repository. The output of the optimization techniques is used to train models based on the two classification algorithms. The measures of prediction were compared. [ABSTRACT FROM AUTHOR]

Drowsiness and Fatigue of drivers square measure among the numerous reasons for street mishaps. Street crashes square measure the chief normal styles of mishaps and passings around the world, and furthermore the significant explanations behind these mishaps square for the most part measure regularly intoxicated, laziness and crazy conduct of the main impetus once per year, they increment the measures of passings and fatalities wounds worldwide. in accordance with the globe Health Organization, street traffic wounds have up to 1.25 billion across the globe, that makes driver languor identification an intense expected space to deflect different rest prompted street mishaps during this paper, a module for Advanced Driver help System (ADAS) is presented to downsize the amount of mishaps because of drivers exhaustion and thusly increment the transportation wellbeing. This technique significantly manages the programmed driver tiredness identification upheld visual information and Machine learning. The model assesses each edge of the video, that assists with recognizing the condition of the main impetus. we tend to blessing each emotional and quantitative results to approve the cases made inside the paper. Communication is the best way of expressing your thoughts, ideas, and feelings. Among all the types of communication technologies, a audio, or a speech, it is the preferred, advanced, and professional means of communication between people. In the era of the Internet of Things is growing, and evolving, with more and more intelligent systems that will be available for day to day use. These types of applications, ranging from simple handheld devices, the structures of a complex of autonomous vehicles and automated systems that are used in various fields. [ABSTRACT FROM AUTHOR]

The SVM is a widely known machine learning, which is very useful for regression applications and pattern classification. These machines have been used successfully in several domains to address numerous real-world challenges. In this context, parameter optimisation for an SVM is a widely researched topic, which has attracted attention from several research domains. Algorithms facilitating optimisation have been of greater interest compared to other algorithms. Algorithmic approaches allow the optimal parameters for an SVM to be determined, after which the model can be adapted for several other applications. During the last two decades, several enhancements have been brought about to facilitate better optimisation of SVM models to offer enhanced performance. This paper focuses on the several algorithms currently employed to optimise support vector machines in their basic and modified forms. This paper comprises a comprehensive analysis of algorithms and aims to ascertain the present challenges relating to algorithms used for SVM parameter optimisation. This study cannot evaluate all the details; however, the significant theoretical aspects are covered using references to existing literature. [ABSTRACT FROM AUTHOR]

This paper mostly focuses on real-time detection and classification of biomedical waste objects. Traditional methods for detecting the objects like biomedical waste objects in machine learning are substituted by recent technology of object detection methods in deep learning by building Convolutional Neural Network (CNN) which is nothing but the components of deep learning. This paper proposes a deep learning approach for object detection of biomedical waste material by evaluating the TensorFlow object detection API, which can used for real time application, this biomedical object detection API further we can used for automate the biomedical waste segregation system, this proposed system has been trained using more than 500 images for each type of biomedical waste object. Tensorflow is useful and easy to object detection that can form powerful image recognition software, in this proposed work we are using Faster R-CNN algorithm for developing this object detection process. [ABSTRACT FROM AUTHOR]

In India, climate change has a huge impact on the agricultural sector. It directly affects crop yield, nutritional quality and productivity. In this paper, a model is devised to correctly predict the crop yield only from past data including factors like rainfall so that appropriate measures can be taken in the future. Machine Learning is one such tool which can be used to solve this issue. Random Forest Algorithm, XGBoost and K-Nearest Neighbor (KNN) Algorithm are the machine learning algorithms used for crop yield prediction in this paper and the one with higher accuracy can be used for future work. Further discussion includes how much impact each factor related to crop yield has on the predicted value so an ordinary farmer can plant and take care of his crops according to it and use this model for his daily work. In doing this, modernization of the agricultural sector is made possible and a boost is given to crop yield in India. [ABSTRACT FROM AUTHOR]

This paper presents a novel idea of implementing & creating a model to determine the fake news services Vs the real news. This is done by implementing the latest Machine Learning algorithms to create models. The model that is being created deploys the latest data sets that have been trained and manipulated and detects, classifies and to recognise and to provide the information that has been collected across various channels to be real or fake news. Data Validation is an ever-growing field, which with the emerging era of data science has increased its manifold tenfold. Thus are the implications of detection and classification of the data. Whomsoever it may be, whether companies, businesses or authorities from the government, it is essential to formulate an application to detect and prevent Fake News spread or misinformation from reaching the mass. This paper implements the latest growing pace of Machine Learning to implement the same. [ABSTRACT FROM AUTHOR]

In this paper a machine learning based solution for detection of public transport the visually impaired people have been proposed. The proposed solution helps visually challenged people to detect cabs and buses. One of the major challenges faced by individuals with complete loss of vision is to navigate around places. Lack of awareness about the public transport facilities available for them in their vicinity is a challenge for the visually impaired. Existing assistive aids are not directed towards solving this problem. The solution proposed in this paper employs five machine learning classifiers namely Decision tree, Random Forest, SVM, Gaussian Naïve Bayes and KNN which are trained on a custom dataset. SIFT is employed for feature detection and PCA is used for dimensionality reduction. The trained model precisely detects cabs and buses, and then provides audio and haptic feedback to the user. The distinct points to note about the solution are the low power requirements and reduced latency both of which are essential for its purpose. In this approach maximum accuracy achieved is 98.1% with Random Forest classifier model. [ABSTRACT FROM AUTHOR]

Now a day there is exponential growth in music files, so the need for automatic music genre classification approach is also increasing. Current classification systems of music genres developed using machine learning algorithms and some of the limitations of existing music genre systems are doesn't work with large database, low accuracy etc. In this paper, presented a large music dataset namely GTZAN with 10 distinct music genres like jazz, rock, hip-hop etc. In order train the classification model of music genre system, a deep learning based technique called CNN (Convolution Neural network) is applied. The proposed method performs the classification of music genre into innumerable genre with the help of feature vector extraction, which plays a vital role in training the model. The feature vector called MFCC (Mel Frequency Cepstral Coefficient) considered in classifying the raw files which are converted to respective music genres or types. The performance of various classification techniques are analyzed compared with respect to evaluation metrics. [ABSTRACT FROM AUTHOR]

This paper presents the systematic literature review of the application machine learning method in detecting a distributed of service (DDoS) attack. Several relevant research papers were selected and they were reviewed based on the method using to provide the best performances and evidence in machine learning technique applications. The researchers are dedicating their efforts to analyzing, summarizing, and evaluating various machine learning methods for detecting DDoS attacks. Therefore, the purpose of this study is to evaluate several machine learning approaches for detecting DDoS attacks in computer networks. These mechanisms are characterized into five categories, the Multiple Linear Regression method, Deep Neural Network (DNN) and Long Short-Term Memory (LSTM) method, Recurrent Neural Network (RNN) with Autoencoder, Deep learning-based method, and LSTM with Singular Value Decomposition (SVD). The paper also debates several open research questions and the research technique, parameters, and metrics. Also reviewed and contrasted were summaries of analyses and gaps in deploying a predictable machine learning model. Thus, the paper is expected to benefit academicians and researchers in developing an efficient solution for the machine learning mentioned above in detecting DDoS attacks. [ABSTRACT FROM AUTHOR]

The time people spend online has been increasing dramatically recently, and people can become anonymous when posting, share their own opinion and participate in online chat. Because of this, more and more people are sexually harassed online on various social media, especially with children. This paper aims to detect sexual harassment at an early phase using three types of machine learning (SVM, Logistic regression, XGBoost) with a bag of words to represent the text. This paper deal with two kinds of data sets Chats sexual predators (CSP) and comments sexual harassment (CSH). In the logistic regression for the datasets (CSH, CSP), the accuracy obtained was 98.44%, 93.71%, while the accuracy of XGBoost was 96.57%, 90.65%, respectively. XGBoost use to avoiding overfitting and shows promising results in both data sets. [ABSTRACT FROM AUTHOR]

This review paper thoroughly examines the prescriptive analytics approach to employee recruitment, focusing on the use of machine learning and mathematical programming to enhance the hiring process and revolutionize HR-related decision-making. It also draws upon valuable insights and practical recommendations from existing literature, providing guidance for organizations seeking to improve their recruitment planning and expansion strategies. Divided into two main sections, the study first explores techniques for extracting insights from the literature to evaluate the potential success of recruitment efforts. The second section presents a dependable mathematical model that comprehensively optimizes the hiring process, considering various organizational-level factors. Additionally, it investigates forecasted hiring success rates using machine learning, incorporating these predictions into a comprehensive recruitment optimization framework that addresses broader parameters and organizational goals. The mathematical programming model offers an effective solution for optimization, even when dealing with balanced demand and diversity requirements, maintaining high accuracy compared to actual recruiter selections. The paper concludes by highlighting limitations in current models for identifying transcription factor binding sites and suggests that variable-order Bayesian networks (VOBN) provide greater flexibility in addressing nucleotide dependencies. [ABSTRACT FROM AUTHOR]

The paper proposes energy prediction techniques on Electric energy consumption from existing electricity supply using machine learning. Machine learning and its techniques are vastly used in factories for maintenance, in industries for optimization and to calculate the fault tolerance to produce an efficient monitoring system. This paper proposes the best model to monitor the usage of power for predicting the energy Raspberry Pi 3Model B+ is the core controller used for this energy prediction and modelling. AC-current, AC-voltage and power are the input parameters measured using current sensor (ACS712) and AC-Voltage sensor (ZMPT101B). MCP3008 ADC is interfaced with the controller to read analogue current and voltage values. Linear regression and logistic regression are used for both prediction and optimization. The two algorithms are implemented on the Spyder IDE which is installed in the Raspberry pi 3Model B+ controller. After reading the input parameter it predicts the energy consumption and displays whether it is high or low for that day. Experimental Results suggests that the linear regression model is the best among the two because the accuracy metric values viz., Mean Absolute Error, R2 score, Execution time, Residual Sum of Square are comparatively less when compared with logistic regression model. [ABSTRACT FROM AUTHOR]

In this paper the focus is to study the use of artificial intelligence and machine learning in acquisition of knowledge for students. industry which can be employed to improve the teaching learning process and make it more flexible for all students resulting in the mapping with the future skills required by the students. The education is undergoing a fast change keeping in view the global trends the new teaching and learning approaches have being examined in a variety of settings. The paper explains AI and machine learning as the future of education in both developed and developing countries. The paper focuses on artificial intelligence and machine learning in future of education. The finding indicates that AI and machine instruction help in instructional method, examination, evaluation and training to staff in education sector although it is still having some challenges in the education system. The study suggests the research to be done about the effectiveness of AI and machine systems in student's evaluation and examination. [ABSTRACT FROM AUTHOR]

This paper reviews the application of big data in smart manufacturing. Currently, the application of big data into the manufacturing operation is still a premature undertaking, with cost, compatibility, and lack of expertise hindering decision-making for many organizations. The selected papers discuss a variety of situations in which big data analytics and its applications have been used to enhance decision-making in the manufacturing operations, productions, scheduling, quality assurance, maintenance, and sustainability with significant data analytics considerations, obstacles to extensive data analytics adoption, machine learning, and use of sensors for data extraction. This article presents the discussions in the manufacturing industry surrounding the use of big data analytics. [ABSTRACT FROM AUTHOR]

This paper analyzed the feature selection for reducing the number of input variables when developing a predictive model. Boruta Algorithm is using in this paper as a wrapper around a Random Forest classification algorithm. Boruta algorithm is one of the algorithms used to determine the significant variables (feature selection) in a classification model in the machine learning approach, as supervised learning. Our results show that on the German Credit Data from the UCI Machine Learning with 20 variables, feature selection using Boruta Algorithm with Python Programming obtains 4 significant features. [ABSTRACT FROM AUTHOR]

Software is becoming more complex, and lengthier in size, and needs to be updated on a time basis. But the constant change in codes has a high chance of arising faults that adversely affect the system performance. To avoid this interruption, and to improve the overall performance of software, a fault prediction system is required. The nature of the fault depends upon various parameters, but it is also important to identify the role of parameters in fault generation. In this paper, four feature selection techniques (Correlation Coefficient, Fisher Score, LASSO, Recursive Feature Selection) are applied on three machine learning methods individually (Random Forest, Logistic Regression, SVM) using Python 3.8 to compare the result for highest performance. The result clearly shows that Random Forest gives the highest accuracy of 95% when combined with the Fisher Score feature selection technique. The outcome shows that rather than using all features of any data set, a selected feature should be used for better fault-free software performance. [ABSTRACT FROM AUTHOR]

Global warming has an impact on people all around the globe, which has a substantial impact on them and is hastening climate change. An accurate forecasting system is required for early detection and enhanced agricultural land management. Rainfall forecasting is a challenging job in reality, and the findings must be precise. Rainfall forecasting is a major problem because of the unreliability of existing methods for predicting rainfall. The purpose of this paper to build an accurate model for the daily prediction of the rainfall in Australia using Python In order to find the optimal model based on testing accuracy, four machine learning algorithms are utilised for training and testing (Logistic Regression, Gaussian Naive Bayes, XGboost classifier, and Random Forest). The data was collected at a variety of meteorological stations around Australia using Kaggle. This data collection has 145460 records and 22 attributes. As a result, in order to find the best suitable technique, a comparative study was undertaken after applying four methodologies. Furthermore, a variety of techniques are used, including multiple linear regression and support vector regression, which may provide the most accurate results (up to 78 %). We observed that our model is less accurate when we compared it to this study. With an accuracy of 84.61 %, the XGBoost classifier surpasses rival approaches. There are several misconceptions, according to the matrix. It will be necessary to combine diverse methodologies in the future to increase prediction accuracy. In addition, new datasets are being used, and new areas are being explored. [ABSTRACT FROM AUTHOR]

The COVID-19 (Corona virus disease 2019) outbreak induced millions of human beings the loss of life, still spread of the disease was unstoppable and was declared a pandemic with the aid of the WHO (World Health Organization). Therefore, human beings across the world are nevertheless getting contaminated every day. Reverse Transcription Polymerase Chain Reaction (RT-PCR) to identify COVID-19 is not feasible in terms of both cost and time of identification, which might cost the patient's life. Therefore, to make identification economical and feasible, researchers were attempting to use clinical images (x-ray and CT etc.,) to detect COVID-19 with the assist of Machine Learning (ML) and Artificial Intelligence (AI) approaches to aid in automating the identification of the pandemic. This paper helps in understanding some ML and AI approaches for detecting COVID-19 from clinical lung images. The amassed records about accessible research sources and inspected a complete of 30 research papers until august 2021. This paper includes the exploration and analysis of data sets, pre-processing methods, segmentation approaches, feature extraction, classification, and test effects, which can be useful for discovering future lookup instructions in the area of automated analysis of COVID-19 sickness the usage of AI-based frameworks. [ABSTRACT FROM AUTHOR]

Object tracking is a vital component of various applications. It plays a significant role in various applications such as machine vision, object/face recognition, and classification. This paper presents the comprehensive study of various object tracking procedures, which are usually carried out in diverse conservational circumstances. Object tracking is the most attractive field of investigation also a game-changer for finding the feature intensities. In this paper, tracking on an object is considered on Colour, shapes, texture, object of interest in single or multiple object detection, motion direction and calculated in multiple frames. Due to the development of machine learning schemes and various other related tools, the evolution of deep learning methods is widely expected. Through investigational assessment of tracking outcomes in the standards in whole and by the assembly, the researcher demonstrates the success of deep-networks for tracking activity in distinct compartments and associate the benefits of these networks and their healthiness under distinct tracking circumstances. Furthermore, they examine the confines of modern approaches and entice some valuable decisions to examine novel directions for multi-object tracking. [ABSTRACT FROM AUTHOR]

Sentiment, emotions, feelings, showing or giving judgments by face gestures, etc. can be communicated by text, speech or images. Analyzing image Polarity via images is now a burgeoning research field. An image can easily be interpreted by any human being and acts as a bridge to relate an image to the human's thoughts. The paper proposed a model for analyzing the sentiments or emotions of an image. We have used a Data-Mining Orange Tool for executing the experiment for classification and sentiment analysis of an image. The main challenge is to acquire the polarity of the unlabeled data using optimal machine learning techniques. To overcome this challenge for getting the polarity and sentiment of an unlabeled data the proposed model used various noteworthy machine learning models like Neural Network, Naïve Bayes and random forest for classifying and analyzing the image sentiment and also provide a comparison based on certain parameters like Area Under Curve, Precision, Recall, classification Accuracy, etc. The tool provides a complete package for performing image analysis using the widgets available with orange tool and a data visualization tool to get a visible representation of the output. The proposed model shows that Random Forest performance the best from the three compared algorithms with classification Accuracy of 0.747, recall & precision as 0.747 & 0.752 and the accuracy based on confusion matrix is 74.72%. [ABSTRACT FROM AUTHOR]

Network security has gained tremendous importance in today's world of interconnected systems, and it is drawing attention of lot of researchers and academicians. Intrusion Detection Systems (IDSs) are an essential aspect of network security. Machine learning (ML) methods can detect and prevent security breaches and improve network and internet security. The paper uses a supervised ML algorithm, namely, Support Vector Machine (SVM), to classify various categories of networking attacks. The standard NSL-KDD dataset is considered to analyze the proposed model performance. Paper uses feature selection technique to further improve the model's performance metrics like accuracy among others. The paper uses a novel approach by combining Infinite Feature Selection (IFS) with Whale Optimization Algorithm (WOA) to select the subset of features. The results analyze the performance of the proposed model based on various parameters such as accuracy, recall, precision, and F-measure. It is observed through experimentation that the proposed feature selection approach improves the accuracy of the SVM-based multiclass classification on NSL-KDD dataset. The effect of various normalization techniques is also analyzed on the model performance and min-max normalization achieves better results than the standard normalization. [ABSTRACT FROM AUTHOR]

The quantity of data on web is increasing as the user of the internet grows. Now, Software is capable enough to replace everything that requires human interference or presence. SDLC (Software Defect LifeCycle) is process that is followed in developing the Software, which comprises of rigorous testing. To avoid wasting time and effort on Bugs, Software Bug Prediction has a crucial role in the early phase of development in maintaining and developing the good quality of Software. Bugs are the small section of code which leads to failure of an application. The focus of modern testing and debugging methodologies has switched from 'detecting' to 'predicting' faults in the code. Software Bug Prediction concerns with overall success as early software prediction can improve the quality, reliability, efficiency, reduced cost of Software. The outcomes accomplished were compared with various other different technique and XGBoost technique defeat them for all dataset. Many machine learning algorithm predicts less accuracies and assumes that characteristics for the dataset creates a problem of biasness and High variance. Current technique also does not perform so well on sparse data and possess low execution speed and accuracy. This paper studies and compares the hidden patterns among software attributes that are extracted using a machine learning approach for 4 dataset from NASA JM1, CM1, KC2, and PC3 using multiple machine learning and ensemble classifiers LogisticRegression, DecisionTree, RandomForest, Adaboost and XGBoost. [ABSTRACT FROM AUTHOR]

Customer Churn Prediction in Banking Sector is the most research area in past few years. Nowadays there isvarious options available for customer in banking sector, where to put money. It is important to gain Customer satisfaction and to maintain bank-customer satisfaction relationship with customer. Generally, if a client stops mistreatment the services of an organization for an extended amount of your time (which varies looking on the merchandise or services of the company), such a client is taken into account churned. In this research paper, a method is proposed to to anticipate client stir in financial area using Machine Learning Algorithms with PCA. In this study Support Vector Machine (SVM), Random Forest Classifier (RFC), Linear Regression (LR), Principal Component Analysis (PCA), Feature selection technique are used to find most relevant features. The dataset is taken from Kaggle. After preprocessing of dataset, the dataset is cleaned from the noisy data. And the dataset is ready for the training process. Now the algorithms are used to train the model. In this using PCA with SVM, PCA with RFC, PCA with LR, Accuracy is measured using confusion matrix. As a result, applying PCA with SVM gives high accuracy as compared to others models. [ABSTRACT FROM AUTHOR]

The objective of this paper is to identify the signature verification system which can overcome individual presence for verification and recognition task of handwritten signature. Amid corona duration it has been found that there is a need of automatic online system which can omit physical presence of person for signature verification. This paper has proposed signature verification model with better accuracy. Novel Architecture of convolution neural network is proposed which exhibited 99% training accuracy on GPDS synthetic Signature database. Writer independent Features of signature were extracted through CNN and these features were fed into machine learning model for classification. Novel architecture is proposed in this paper. [ABSTRACT FROM AUTHOR]

Recommendation engines are one of the most popular machine learning techniques in the current era. In today's market today it helps to increase the profit or achieve the target in a long way. Showing relevant products in online markets or recommending movies according to a user's interest drives much more content engagement of a user. It generates a custom tailor-made recommendations for each customer. These things can be achieved by various means and one of such methods is clustering. Clustering enables to find or generate hidden patterns in data. A Recommendation can be transformed into a clustering problem by comparing and determining how close the two things are so that they can be suggested. The goal of this paper is to compare and contrast recommendations based on K-means and Fuzzy C-means clustering. This paper also tries to draw the benefits of using Fuzzy C-means clustering method in recommendation systems to enhance the suggestions. Also, variation in the value of partition coefficient of Fuzzy C-means clustering is analyzed with the change in number of clusters which signify the clustering quality in some manner. The dataset taken for this purpose is a movie dataset and users are recommended the movies featuring in the dataset. [ABSTRACT FROM AUTHOR]

In multimedia content, text play major role for transmission and it is crucial to protect text data while transmitting over network. This can be achieved by text encryption algorithm. However, the method relies on many resources that increase the processing unit time, memory, and battery power. There are so many different techniques should be used to protect confidential text from unauthorized access. This paper summarizes on the existing works which used different techniques for text encryption by using deep learning algorithms. Combination of these approaches helps to analyze different algorithms for different text, accelerates the processing time while maintaining accurate preservation and retrieval. [ABSTRACT FROM AUTHOR]

In the present era of the Fourth Industrial Revolution, i.e., 4IR or Industry 4.0, the digital world has witnessed enormous technological advancement. Artificial Intelligence (AI), Machine learning (ML) such as supervised learning (SL), unsupervised learning (USL), semi-supervised learning, and reinforcement learning (RL) are the most exciting recent technologies in today's world. Learning algorithms such as web search engines like Google or Yahoo, E-Mail filtering, Social Media, Airlines, E-hailing ride/price matching are the most valuable features of ML in our daily lives. The e-hailing, which is a process of ordering a cab or taxi online through a digital app, relies on machine learning to perform ride fare calculations. This paper briefly explains how e-hailing providers build dynamic pricing using reinforcement learning and associated problems in the existing pricing algorithm. Often dynamic pricing terms are unfair/discriminated pricing, and it remains controversial amongst consumers. The article discusses how supervised learning can help address pricing issues linked to the dynamic pricing algorithm's reinforcement learnings. The expected outcome of this paper is to help play a reference in creating a practical solution that can resolve pricing issues that float around dynamic pricing algorithms. [ABSTRACT FROM AUTHOR]